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Proceedings Paper

Local hyperspectral data multisharpening based on linear/linear-quadratic nonnegative matrix factorization by integrating lidar data
Author(s): Fatima Zohra Benhalouche ; Moussa Sofiane Karoui; Yannick Deville; Abdelaziz Ouamri
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Paper Abstract

In this paper, a new Spectral-Unmixing-based approach, using Nonnegative Matrix Factorization (NMF), is proposed to locally multi-sharpen hyperspectral data by integrating a Digital Surface Model (DSM) obtained from LIDAR data. In this new approach, the nature of the local mixing model is detected by using the local variance of the object elevations. The hyper/multispectral images are explored using small zones. In each zone, the variance of the object elevations is calculated from the DSM data in this zone. This variance is compared to a threshold value and the adequate linear/linearquadratic spectral unmixing technique is used in the considered zone to independently unmix hyperspectral and multispectral data, using an adequate linear/linear-quadratic NMF-based approach. The obtained spectral and spatial information thus respectively extracted from the hyper/multispectral images are then recombined in the considered zone, according to the selected mixing model. Experiments based on synthetic hyper/multispectral data are carried out to evaluate the performance of the proposed multi-sharpening approach and literature linear/linear-quadratic approaches used on the whole hyper/multispectral data. In these experiments, real DSM data are used to generate synthetic data containing linear and linear-quadratic mixed pixel zones. The DSM data are also used for locally detecting the nature of the mixing model in the proposed approach. Globally, the proposed approach yields good spatial and spectral fidelities for the multi-sharpened data and significantly outperforms the used literature methods.

Paper Details

Date Published: 15 October 2015
PDF: 9 pages
Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430Y (15 October 2015); doi: 10.1117/12.2194904
Show Author Affiliations
Fatima Zohra Benhalouche , Institut de Recherche en Astrophysique et Planétologie, CNRS, Univ. de Toulouse (France)
Univ. des Sciences et de la Technologie, Oran (Algeria)
Moussa Sofiane Karoui, Institut de Recherche en Astrophysique et Planétologie, CNRS, Univ. de Toulouse (France)
Univ. des Sciences et de la Technologie, Oran (Algeria)
Ctr. National des Techniques Spatiales (Algeria)
Yannick Deville, Institut de Recherche en Astrophysique et Planétologie, CNRS, Univ. de Toulouse (France)
Abdelaziz Ouamri, Univ. des Sciences et de la Technologie, Oran (Algeria)


Published in SPIE Proceedings Vol. 9643:
Image and Signal Processing for Remote Sensing XXI
Lorenzo Bruzzone, Editor(s)

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